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NCT06604663: CASCADE
Data Science and Qualitative Research for Decision Support in the HIV Care Cascade
NA trial testing Activation of the CDSS system in Human Immunodeficiency Virus in 80,000 participants. Enrolling by invitation.
31 January 2026
Quick facts
| Lead sponsor | Brown University |
|---|---|
| Phase | NA |
| Status | ENROLLING BY INVITATION |
| Study type | INTERVENTIONAL |
| Allocation | randomized |
| Design | parallel |
| Masking | none |
| Primary purpose | health services research |
| Enrollment | 80,000 |
| Start date | 20 May 2024 |
| Primary completion | 31 January 2026 |
| Estimated completion | 31 October 2026 |
| Sites | 1 location across Kenya |
Drugs / interventions tested
- Activation of the CDSS system
Conditions studied
- Human Immunodeficiency Virus — all drugs for Human Immunodeficiency Virus →
- Treatment Adherence — all drugs for Treatment Adherence →
- Treatment Compliance — all drugs for Treatment Compliance →
- Patient No Show — all drugs for Patient No Show →
Sponsor
Brown University
Who can join
Adults 18 to 100, any sex, with Human Immunodeficiency Virus or Treatment Adherence. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere. There are two main questions to be answered. First, can the prediction algorithms accurately identify those who are at risk for (a) missing scheduled clinic visits and/or (b) treatment failure, evidenced by elevated HIV viral load? And second, can the risk predictions be used in a structured way to (a) improve retention in care and/or (b) reduce the number of patients having elevated viral load? Researchers will develop machine learning prediction algorithms, incorporate the risk prediction information into the electronic health record, provide guidance to clinical health workers on use of the point-of-care interface tools that display risk prediction information, and incorporate feedback from clinic staff to modify and co-develop the protocol for using risk predictions for improving patient outcomes. They will then compare the proportion of patients having missed visits and longer-term loss to follow up, and the proportion with elevated viral load, between clinics that use the information from the risk prediction algorithms and those that do not.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT06604663
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06604663 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Brown University
- Last refreshed: 12 January 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06604663.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing